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Evaluation of impurities in simvastatin drug products with the use of FT-IR spectroscopy and selected chemometric techniques

100%
Kupcewicz B., Ronowicz J., Balcerowska-Czerniak G., Walasek A., Budzisz E.
Open Chemistry
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2013
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vol. 11
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issue 8
1320-1329
EN
In the present study a reversed phase high performance liquid chromatography (RP-HPLC) method with diode array detector (DAD) at room temperature was used for obtaining impurity profiles of 20 drug products containing simvastatin as an active substance. Fourier-transform infrared spectroscopy (FT-IR) was carried out to obtain absorption spectra of samples. The partial least squares (PLS) model was built to predict the relative content of lovastatin, the main impurity of simvastatin, and sum of statin-like impurities. In order to build the PLS model, peak areas obtained from HPLC chromatograms were related to FT-IR spectra of drugs. The PLS model based on signal normal variate and orthogonal signal correction (SNV+OSC) transformed FT-IR spectra was able to predict the content of drug impurities in real samples with a good prediction ability (R2 > 0.95). [...]
2
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Simultaneous analysis of first-line anti-tuberculosis drugs in tablets by UV spectrophotometry compared to capillary zone electrophoresis

100%
Marcellos L., Faria A., Souza M., Almeida M., Sabin G., Poppi R., Oliveira M.
Open Chemistry
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2012
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vol. 10
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issue 6
1808-1816
EN
The development and optimization of a novel UV spectrophotometric methodology was proposed for simultaneous analysis of ethambutol (ETB), isoniazid (ISO), rifampicin (RIF) and pyrazinamide (PYR), using multivariate calibration based on the partial least squares method (PLS). The methodology was successfully applied for analysis of four-drug fixed dose combination (4-FDC) tablets used for tuberculosis treatment. A 34 Box-Behnken design, with triplicate in central point, was used for sample preparation in the calibration step. In the present case, nine latent variables were chosen for the model development that presented the smallest RMSECV and explain 98.76% of data variance in Y block (concentrations of ETB ISO, RIF and PYR) and 99.93% of data variance in X block (spectral data). PLS models for ETB, ISO, RIF and PYR presented RMSEP and R2 values of 0.23 mg L−1 and 0.971; 0.14 mg L−1 and 0.731; 0.11 mg L−1 and 0.990 and 0.57 mg L−1 and 0.972, respectively. A validation step was performed based on the comparison between the UV spectrophotometric proposed methodology and capillary zone electrophoresis (CZE) in 4-FDC real samples and no significant difference was found between two methodologies at 95% of confidence level. [...]
3
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Bromatological, analytical and chemometric assessment of animal and plant foods based on mineral composition

100%
Szefer P., Grembecka M.
European Journal of Translational and Clinical Medicine
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2022
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vol. 5
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issue 1
77-106
EN
There are several examples of numerous applications of analytical and multivariate techniques useful in investigations of varied assortment of food products. The successful use of chemometrics in study of food such as meat and its products, fish, seafood, milk and dairy products, honey, cereal products, oils, oilseeds and nuts, vegetables, fruits, mushrooms, tea, coffee, confectionary products, mineral waters and alcoholic beverages deserves attention. RDA indicated exceeded its normative values for Se, Cu, Mn, Fe and Cr in some groups of animal food and Cr, Mn, P and Fe in some assortment of plant food. Based on PTWI values for Pb, Cd and Hg, there is no threat to human health resulting from the consumption of the investigated food products. It is concluded that the proper use of analytical and chemometric tools is useful for assessing nutritive and health quality of animal and plant foods. They play an important role in quality control, and their classification in view of geographical origin, confection and degree of environmental pollution. Both, instrumental and multivariate techniques would be useable in differentiating unprocessed and technologically processed food as well as detecting fraud to preserve the brand name of the original product. The aim of this study is to give an overview of the crucial issues associated with the implementation of chemometrics in food research and development.
4
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Statistical interpretation of medical data of patients with alcohol abuse

100%
Bardarov V., Simeonova P., Neikova L., Bardarov K., Simeonov V., Tsakovski S., Kanev K.
Open Medicine
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2012
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vol. 7
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issue 4
465-474
EN
An attempt is made to assess a set of biochemical, kinetic and anthropometric data for patients suffering from alcohol abuse (alcoholics) and healthy patients (non-alcoholics). The main goal is to identify the data set structure, finding groups of similarity among the clinical parameters or among the patients. Multivariate statistical methods (cluster analysis and principal components analysis) were used to assess the data collection. Several significant patterns of related parameters were found to be representative of the role of the liver function, kinetic and anthropometric indicators (conditionally named “liver function factor”, “ethanol metabolism factor”, “body weight factor”, and “acetaldehyde metabolic factor”). An effort is made to connect the role of kinetic parameters for acetaldehyde metabolism with biochemical, ethanol kinetic and anthropometric data in parallel.
5
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Chemometrics as an option to assess clinical data from diabetes mellitus type 2 patients

88%
Nikolov M., Simeonova P., Simeonov V.
Open Medicine
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2009
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vol. 4
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issue 4
433-443
EN
The present study deals with the application of two major multivariate statistical approaches - Cluster Analysis (CA) and Principal Components Analysis (PCA) as an option for assessment of clinical data from diabetes mellitus type 2 patients. One hundred clinical cases of patients are considered as object of the statistical classification and modeling, each one of them characterized by 34 various clinical parameters. The goal of the study was to find patterns of similarity, both between the patients and the clinical tests. Each group of similarity is interpreted revealing at least five clusters of correlated parameters or five latent factors, which determine the data structure. Relevant explanation of the clustering is found based on the pattern of similarity like glucose level, anthropometric data, enzyme level, liver function, kidney function etc. It is assumed that this classification could be of help in optimizing the performance of clinical test for this type of patients and for designing a pattern for the role of the different groups of test in determining the metabolic syndrome of the patients.
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